mega:gdr:bib

The Oxford Handbook of Random Matrix Theory, Edited by G. Akemann, J. Baik, P. Di Francesco, Oxford University Press, 2011.
An Introduction to Random Matrices, G. Anderson, A. Guionnet, O. Zeitouni, Cambridge University Press, 2009. here.
Spectral analysis of large dimensional random matrices, Z.D. Bai, J.W. Silverstein Second Edition, Springer, New York, 2009.
Topics in Random Matrix Theory, T. Tao, Graduate Studies in Mathematics, AMS, 2012.
Eigenvalue Distribution of Large Random Matrices, L. Pastur, M. Shcherbina, Mathematical Surveys and Monographs, AMS, 2011.
Notes on random matrices, C. Bordenave. here.
Around the circular law, C. Bordenave, D. Chafaï, here.
Several applications of the moment method in random matrix theory, S. Sodin, Proc. International Congress of Mathematicians 2014. here.
Lecture Notes on Random Matrices, V. Kargin, E. Yudovina, here.

Random Matrices, M.L. Mehta. Second Edition, Academic Press, Boston, 1991.
Log-gases and random matrices, P. J. Forrester, Princeton University Press, Princeton, NJ, 2010.
Orthogonal Polynomials and Random Matrices: A Riemann-Hilbert Approach, P. Deift, Courant Lecture Notes, AMS, 2000.
Random Matrix Theory: Invariant Ensembles and Universality, P. Deift, D. Gioev, Courant Lecture Notes, AMS, 2009.
A Survey on the Eigenvalues Local Behavior of Large Complex Correlated Wishart Matrices, W. Hachem, A. Hardy, J. Najim, ESAIM: Proceedings and Surveys, 2015. here.
The Surprising Mathematics of Longest Increasing Subsequences, D. Romik, Cambridge University press, 2015. here.
Combinatorics and Random Matrix Theory, J. Baik, P. Deift, T. Suidan, Graduate Studies in Mathematics, AMS, 2016.
Longest increasing subsequences: from patience sorting to the Baik-Deift-Johansson theorem, D. Aldous, P. Diaconis, Bull. Amer. Math. Soc. (N.S.) 36 (1999), no. 4, 413–432.
Lectures on integrable probability, A. Borodin, V. Gorin. here.
Random matrices and determinant processes, K. Johansson, Mathematical statistical physics, 1–55, Elsevier B. V., Amsterdam, 2006. here.
Kardar-Parisi-Zhang universality, I. Corwin, Notices Amer. Math. Soc. 63 (2016), no. 3, 230–239. here.
Random matrices, B. Valko (with Valko-Virag's approach to beta-ensembles). here.

Free probability for probabilists, P. Biane, MSRI Publications, 1998. here.
Lectures on the combinatorics of free probability, A. Nica, R. Speicher. Cambridge University Press, Cambridge, 2006.
The Semicircle Law, Free Random Variables and Entropy, F. Hiai, D. Petz, Mathematical Surveys and Monographs, 2000.
Free random variables, D. Voiculescu, K. Dykema, A. Nica, CRM Monograghs Series No.1, Amer. Math. Soc., Providence, RI, 1992.
Large Random Matrices: Lectures on Macroscopic Asymptotics, A. Guionnet, École d'Été de Probabilités de Saint-Flour XXXVI – 2006.
Free probability and random matrices, R. Speicher, Proc. International Congress of Mathematicians 2014. here.
Free probability and random matrices, J. A. Mingo and R. Speicher, ongoing project. here
Three lectures on free probability, J. Novak, M. LaCroix. here.
Lecture Notes on Free Probability, V. Kargin, here.
What is Free Probability?, R. Speicher, here.

Universality for random matrices and log-gases, L. Erdös, 2012. here.
Universality of local spectral statistics of random matrices, L. Erdös, H.-T. Yau, Bull. Amer. Math. Soc. 49(3), 377-414. here.
Dynamical approach to random matrix theory, L. Erdös, H.-T. Yau (ongoing project). here.
Lectures on the local semicircle law for Wigner matrices, F. Benaych-Georges, A. Knowles. To appear in SMF volume Panoramas et Synthèses (2017). here.

Random graphs, B. Bollobas, Cambridge University Press, 2009.
Spectral Graph Theory, F. Chung, CBMS Regional Conference Series in Mathematics, 1997.
Spectral measures of random graphs, C. Bordenave. To appear in SMF volume Panoramas et Synthèses (2017). here.

Alice and Bob meet Banach (The Interface of Asymptotic Geometric Analysis and Quantum Information Theory), G. Auburn, S. Szarek (ongoing project). here.
Two-dimensional Markovian holonomy fields, T. Lévy, 2008. here.

Random Matrix Theory and Wireless Communications, A. Tulino, S. Verdu, 2004.
Random Matrix Methods for Wireless Communications, R. Couillet, M. Debbah, Cambridge University Press, 2011.

Four lectures on probabilistic methods for data science, R. Vershynin. 2016 PCMI Summer School, AMS. here
Textbook in high dimensional probability with applications in data science, ongoing project, R. Vershynin. here
Introduction to the non-asymptotic analysis of random matrices, R. Vershynin, Compressed sensing, 210–268, Cambridge University Press, 2012. here
Interactions between compressed sensing, random matrices, and high dimensional geometry, D. Chafaï, O. Guédon, G. Lécué, A. Pajor. Panoramas et Synthèses 37, SMF, 2012. here.
An Introduction to Matrix Concentration Inequalities, J. A. Tropp. Found. Trends Mach. Learning, Vol. 8, num. 1-2, pp. 1-230, May 2015. here.
High Dimensional Statistics, P. Rigollet, here.
Ten lectures and forty-two open problems in the mathematics of data science, A. Bandeira here.
A Mathematical Introduction to Compressive Sensing, S. Foucart, H. Rauhut, Birkäuser, 2013.
Introduction to High-Dimensional Statistics, C. Giraud, CRC Press, 2014.
High Dimensional Statistical Inference and Random Matrices, I. M. Johnstone, Proc. International Congress of Mathematicians 2006, 307-333. here.

Random matrices: high dimensional phenomena, G. Blower, London Mathematical Society, 2009.
The Concentration of Measure Phenomenon, M. Ledoux, AMS, 2005.
Concentration Inequalities: A Nonasymptotic Theory of Independence, S. Boucheron, G. Lugosi, P. Massart, Oxford Univ. Press, 2013.
A transportation approach to universality in random matrix theory, A. Figalli, Boll. Unione Mat. Ital., 10 (2017), no. 1, 55-74. here.